Ambulatory sleep scoring using accelerometers-distinguishing between nonwear and sleep/wake states.

ABSTRACT:

Background

Differentiating nonwear time from sleep and wake times is essential for the estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is required. Therefore, we developed a classification algorithm to determine nonwear, sleep and wake periods from accelerometer data. Our work aimed to (I) develop a new pattern recognition algorithm for identifying nonwear periods from actigraphy data based on the influence of respiration rate on the power spectrum of the acceleration signal and implement it in an automatic classification algorithm for nonwear/sleep/wake states; (II) address motion artifacts that occur during nonwear periods and are known to cause misclassification o

SUBMITTER: Barouni A 

PROVIDER: S-EPMC6942683 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature